SA-TWG Webinar: New Frontiers in One-Bit Signal Processing: From Sample Abundance to Efficient Intelligence at Scale
Date: 13-November-2025
Time: 11:00 AM ET (New York Time)
Presenter: Dr. Mojtaba Soltanalian
About this topic:
The journey begins with a familiar parable: A group of villagers hears that a strange and enormous creature from India has arrived. Eager to understand it, they sneak into the dark barn where it’s kept. One touches the trunk and says, “It’s like a snake!” Another, feeling a leg, declares, “No, it’s like a tree trunk!” A third, touching an ear, insists, “It’s like a fan!” Who is right? The truth is, none of them sees the whole elephant. But what if they had more points of contact? Even if each touch lacked fine detail, the abundance of samples would provide a much better picture of the creature; this is the magic of one-bit sampling. Another core insight is the sample abundance singularity: We have shown in our prior work that, under quantization, increasing data volume can reduce complexity in a counterintuitive manner. Finally, the talk explores inference models that trade bit-depth for bit-width; i.e., decreasing the bit-per-link rate but simultaneously increasing the number of layers for more sophisticated representation (noting that neural networks, in essence, are approximators or “samplers” of an exact mapping).
About the presenter:
Mojtaba Soltanalian (SM) received the B.Sc. degree from Sharif University of Technology and the Ph.D. degree in electrical engineering (Signal Processing) from the Department of Information Technology, Uppsala University, Sweden in 2009 & 2014.
He is currently an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago (UIC) Chicago, IL USA. Prior to joining UIC, he held research appointments at the California Institute of Technology (Caltech) and at the Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg. His research interests include optimization, statistical signal processing, and machine learning, with emphasis on principled signal design for sensing and communications, few-bit and mixed-precision techniques, and the foundations of scalable, reliable AI.
Dr. Soltanalian served on the editorial boards of the IEEE Transactions on Signal Processing and the IEEE Transactions on Aerospace and Electronic Systems (Radar Systems) and chairs the IEEE Signal Processing Society Chicago Chapter. His work has been recognized with several distinctions, including the IEEE Signal Processing Society Young Author Best Paper Award and the European Association for Signal Processing Best Ph.D. Award.
